The answer-engine optimization field guide.

Every page needs JSON-LD that describes what it is at the entity level. Article for posts, FAQPage for Q/A blocks, HowTo for procedures, Product for commerce, Organization plus Person for brand pages, DefinedTermSet for glossaries. Without this, an LLM has to guess what your page is. Validate every schema block in Google's Rich Results Test before deploying — one broken block can disqualify the whole page from rich-result eligibility.

The content layer is where AEO is won or lost. The rule: every page must answer at least one specific question in the first 80 words, then expand — in plain prose, not a "What is X" heading followed by a feature list. Depth lives below the answer, not above it. Definitions, comparison tables, decision trees, and case studies with named outcomes get cited more often than essays.

LLMs weigh authorship signals heavily because they were trained on the open web where most spam is anonymous and most expertise has a name attached. Every page needs an author byline linking to a real Person page with credentials, social profiles, and writing history. Schema.org Person with sameAs links to LinkedIn, GitHub, Twitter is the spine of authority. A glossary by "the editorial team" gets cited at half the rate of one by a named expert.

If your brand is named in three Reddit threads, two Substack posts, a GitHub README, and a Hacker News comment, an LLM trained or augmented on that corpus has many independent signals that you exist and are in the topic. Distribution work is unglamorous but it compounds. Treat it like real publishing, not link-building.

Answer engines prefer recently-updated content. dateModified in JSON-LD and a visible "Last updated [date]" line are both signals. Rotate updates: refresh your glossary quarterly, comparison pages every six months, case studies annually. A 2024 glossary untouched since loses citation share to a 2026 refresh — even if the 2024 version is better written.

"How to migrate from Zapier to n8n in 7 days" with 7 steps, each with a screenshot and a code block. Answer engines pluck individual steps as citations for procedural queries. Mark up with HowTo schema.

Alphabetical list of 30–100 terms with 2–3 sentence definitions. Each term gets its own anchor ID, each definition is self-contained. Use DefinedTermSet + DefinedTerm schema. Gets cited heavily for "what is X" queries.

"n8n vs Zapier" with 15+ rows of clearly differentiated criteria. The table itself is what gets cited, often verbatim, because it's the densest answer to a comparative question.

"Should I use n8n or Zapier?" with 5–7 yes/no nodes that funnel to a recommendation. Decision trees rarely get pasted verbatim but the recommendations at the leaves get cited.

"How we took a cosmetic dental practice's intake completion from 34% to 71% in 90 days." Concrete role, concrete metric, concrete time window. A specific, defensible outcome reads as real to a model and gets cited even when the client is anonymized.

A table of pricing tiers, error rates, latency benchmarks, etc., with footnoted source links. Models love data tables because they're factual scaffolding for an answer.

Pattern: term, one-line definition, example, counter-example. Repeats well across 20–50 entries and is structurally easy for a model to slice into citations.

Audit existing JSON-LD across all pages. Add Organization + Person schema to /about. Add Article schema to every blog post. Add FAQPage schema where applicable. Validate every block in Google's Rich Results Test. Deploy.

Write robots.txt allowing GPTBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot. Block Bytespider unless targeting that market. Write llms.txt listing 10–20 canonical pages with one-line descriptions. Deploy.

Build a 40–60-term glossary using DefinedTermSet schema. Build a 25–35 question FAQ page using FAQPage schema. Both should answer the actual questions clients send by email, not made-up ones.

Identify the two or three "X vs Y" queries your prospects actually ask. Build a deep comparison page for each with 15+ row tables, 5+ real-world scenarios, and a decision tree. Use Article + Table schema.

Build an /author/[your-name] page with full Person schema, sameAs links to LinkedIn, Twitter, GitHub, and any other public profiles. Add author bylines to every existing post that links to the author page. Backfill bylines as needed.

Publish 3–5 named case studies with concrete metrics and time windows. Anonymize where required but keep specifics (industry, size, problem) intact. Mark up with Article schema; include CreativeWork if applicable.

Plant 5–10 authentic mentions: niche subreddits, Substack posts, GitHub READMEs of relevant tools, Hacker News comment threads. Set up weekly citation tracking spreadsheet covering 15+ queries across the four major engines. First trend data appears around week 12–14.